Ehlers-Danlos Syndrome: Systematic Data Querying Finds Novel Variant

Ehlers-Danlos syndrome (EDS) is a group of heritable disorders in which collagen synthesis and fibril formation is disrupted. Consequently, individuals experience articular hypermobility, lax joints, and fragile tissue, skin, and blood vessels (depending on the subtype). These symptoms can lead to significant disability. EDS is transmitted through autosomal-dominant, autosomal-recessive or x-linked inheritance. Desai at al. (2016) performed a genomic analysis of a family with autosomal-dominant inheritance EDS to identify the mutation that causes the dermatosparaxis EDS classification (type VII C).¹

The investigators identified the family through a systematic data query of de-identified electronic medical records from over 80,000 individuals recruited to a biorepository over a six-year period as part of a project called “A Study of the Genetic Causes of Complex Pediatric Disorders.” This study was a large-scale biorepository recruitment drive to study common and rare medical disorders at the Center for Applied Genomics at the Children’s Hospital of Philadelphia.

The research team cross-referenced the biorepository with rare-disease codes (as defined by the Online Mendelian Inheritance in Man database and the International Statistical Classification of Diseases and Related Health Problems 9). In doing so, they identified a nine-year-old male child whose medical records indicated EDS and included a detailed family history and medical history for both parents. They approached the family, who consented to participate in the study and allow researchers to use de-identified data.

The participant’s family history indicated that he had a seven-year-old sister with evidence of EDS. Furthermore, they found evidence within his medical records that his father, paternal uncle and paternal grandmother had phenotypes that indicated EDS-like symptoms. The family history on the maternal side did not indicate EDS-like symptoms. Desai et al. sequenced the whole genome of the mother, the father and the participant. For validation, they performed Sanger sequencing on the affected sister and a half brother.

The investigators were able to perform the data query and subsequent study because their broad consent protocol included DNA extraction, blood/serum collection, access to electronic medical records, and permission to perform a wide range of genomic studies in consenting individuals. Their medical records are also interfaced with three laboratory information systems, meaning that they could access all laboratory results for each individual in the repository.

Desai et al. screened the EDS patient for mutations in ADAMTS2, which is known to be associated with this subtype. The screen returned a negative result for this and all other known EDS-associated genes. He also had no abnormalities in his collagen/procollagen screen. Nonetheless, the researchers believed that the pattern of EDS-like symptoms within his family indicated an inheritance pattern consistent with autosomal-dominant inheritance. So, when they performed a whole-genome analysis, they compared the father and the participant and found 248,772 single nucleotide variants that the two shared but were not present in the mother. When the team compared these with a manual curation and literature survey of the 52 candidate loci, they discovered a mutation (c.1261G > A, p. Gly421Ser) in the ADAMTSL2 gene. They subsequently identified this heterozygous mutation in the participant’s father and affected sister.

This study demonstrated a successful case study of systemic data querying. Desai et al. were able to use phenotypic data and cross-reference it against disease codes to identify genetic causes of rare diseases. They note their fortunate position in having a biorepository that is part of a research and treatment hub, which provides them with a rich sample source. They note that this study has opened the door to future work to better understand the function of ADAMTSL2 in contributing to EDS.

Reference

1. Desai, A., et al. (2016) “Systematic data-querying of large pediatric biorepository identifies novel Ehlers-Danlos syndrome variant,” BMC Musculoskeletal Disorders, 17(80). doi: 10.1186/s12891-016-0936-8.